Theses and Dissertations

Issuing Body

Mississippi State University


King, Roger L.

Committee Member

Younan, Nicholas H.

Committee Member

Lawrence, Gary W.

Date of Degree


Document Type

Graduate Thesis - Open Access


Electrical Engineering

Degree Name

Master of Science


James Worth Bagley College of Engineering


Department of Electrical and Computer Engineering


Rotylenchulus reniformis is a nematode species affecting the cotton crop and quickly spreading throughout the southeastern United States. Effective use of nematicides at a variable rate is the only economic counter measure. It requires the intraield variable nematode population, which in turn depends on the collection of soil samples from the field and analyzing them in the laboratory. This process is economically prohibitive. Hence estimating the nematode infestation on the cotton crop using remote sensing and machine learning techniques which are cost and time effective is the motivation for this study. In the current research, the concept of multi-temporal remote sensing has been implemented in order to design a robust and generalized Nematode detection regression model. Finally, a user friendly web-service is created which is gives trustworthy results for the given input data and thereby reducing the nematode infestation in the crop and their expenses on nematicides.



Rotylenchulus reniformis||Support Vector Regression||Kernel Principal Component Analysis